Tracking objects using particle filters

This paper describes an implementation of particle filter tracker based on condensation algorithm. The filter processes measurements as they become available in a standard predict-update loop. The prediction phase uses the available dynamic model to predict the probability density function in the next time step, by applying both the deterministic and stochastic component of the model to all samples. In the update phase the new measurement is used to update the probability density function by updating the weight of each sample. The goal of this work was to investigate the possibilities of object tracking without learning a dynamic motion model. Changes to the basic algorithm have been implemented that can help to improve the tracking performance by using more than one motion model and more than one predict-update iteration per measurement.